Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI

Xirui Hou; Pengfei Guo; Puyang Wang; Peiying Liu; Doris D M Lin; Hongli Fan; Yang Li; Zhiliang Wei; Zixuan Lin; Dengrong Jiang; Jin Jin; Catherine Kelly; Jay J Pillai; Judy Huang; Marco C Pinho; Binu P Thomas; Babu G Welch; Denise C Park; Vishal M Patel; Argye E Hillis; Hanzhang Lu

doi:10.1038/s41746-023-00859-y

Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI

NPJ Digit Med. 2023 Jun 21;6(1):116. doi: 10.1038/s41746-023-00859-y.

Authors

Xirui Hou^{1

2}, Pengfei Guo³, Puyang Wang⁴, Peiying Liu⁵, Doris D M Lin², Hongli Fan^{1

2}, Yang Li², Zhiliang Wei^{2

6}, Zixuan Lin², Dengrong Jiang², Jin Jin⁷, Catherine Kelly⁸, Jay J Pillai^{2

9}, Judy Huang⁹, Marco C Pinho¹⁰, Binu P Thomas¹⁰, Babu G Welch^{11

12}, Denise C Park¹², Vishal M Patel^{3

4}, Argye E Hillis⁸, Hanzhang Lu^{13

14

15}

Affiliations

¹ Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
² The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
³ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
⁴ Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
⁵ Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
⁶ F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
⁷ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
⁸ Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁹ Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
¹⁰ Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA.
¹¹ Department of Neurologic Surgery, UT Southwestern Medical Center, Dallas, TX, USA.
¹² Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA.
¹³ Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA. hanzhang.lu@jhu.edu.
¹⁴ The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA. hanzhang.lu@jhu.edu.
¹⁵ F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA. hanzhang.lu@jhu.edu.

Abstract

Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging methods hold great promises for early stratification, but at present lack the sensitivity for personalized prognosis. Resting-state functional magnetic resonance imaging (rs-fMRI), a powerful tool previously used for mapping neural activity, is available in most hospitals. Here we show that rs-fMRI can be used to map cerebral hemodynamic function and delineate impairment. By exploiting time variations in breathing pattern during rs-fMRI, deep learning enables reproducible mapping of cerebrovascular reactivity (CVR) and bolus arrival time (BAT) of the human brain using resting-state CO₂ fluctuations as a natural "contrast media". The deep-learning network is trained with CVR and BAT maps obtained with a reference method of CO₂-inhalation MRI, which includes data from young and older healthy subjects and patients with Moyamoya disease and brain tumors. We demonstrate the performance of deep-learning cerebrovascular mapping in the detection of vascular abnormalities, evaluation of revascularization effects, and vascular alterations in normal aging. In addition, cerebrovascular maps obtained with the proposed method exhibit excellent reproducibility in both healthy volunteers and stroke patients. Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging.

Abstract

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